{
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"
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"
    }
   },
   "cell_type": "markdown",
   "id": "a1622735-45ae-4efb-9bf2-3d8ef512f06f",
   "metadata": {},
   "source": [
    "## 【DataFrame结构】\n",
    "- DataFrame是Pandas的重要结构之一，也是在使用Pandas进行数据分析过程汇总最常用的结构之一。掌握了DataFrame的用法，就拥有了学习数据分析的基本能力。\n",
    "### 认识DataFrame结构\n",
    "- DataFrame一个表格型的数据结构，既有【行标签（index）】，又有【列标签(columns)】，它也被称为异构数据表，所谓异构，指的是表格中每列的数据类型可以不同。每一行表示一个条目的信息，每一列表示一个属性。\n",
    "![image.png](attachment:e103c610-18cc-486b-8208-666b1eca5c7a.png)\n",
    "- DataFrame的每一行数据都可以看成一个Series结构，只不过，DataFrame为这些行中的每个行中的数据增加了一个列标签，因此，DataFrame其实是从Series的基础上演变而来的。在数据分析中DataFrame的应用非常广泛，因为它描述数据更为清晰、直观\n",
    "- DataFrame结构类似于Excel的表格型，表格中列标签的含义为：\n",
    "![image.png](attachment:c0f5cf94-49b5-4f53-99c0-65ee35eda696.png)\n",
    "  - Regd.No: 表示等级的序号\n",
    "  - Name: 学生姓名\n",
    "  - Marks: 学生分数\n",
    "- 【注意：同Series一样，DataFrame自带行标签索引，默认为‘隐式索引’即从0开始依次递增，行标签与DataFrame中的数据项一一对应】\n",
    "- 【DataFrame每一列的标签值允许使用不同的数据类型】\n",
    "- 【DataFrame是表格类型的数据结构，具有行和列】\n",
    "- 【DataFrame中的每个数据值都可以被修改】\n",
    "- 【DataFrame结构的行数、列数允许增加或删除】\n",
    "- 【DataFrame有两个方向的标签轴，分别是行标签和列标签】\n",
    "- 【DataFrame可以对行和列执行运算】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9f9b273-0282-433e-957c-0c962a16b1ea",
   "metadata": {},
   "source": [
    "### 创建DataFrame对象\n",
    "- 格式为:\n",
    "- import pandas as pd\n",
    "- pd.DataFrame(data, index, columns, dtype, copy)\n",
    "- data: 输入的数据，可以是'ndarray', series, list, dict.标量以及一个'DataFrame'\n",
    "- index: 行标签，如果没有传递index值，则默认行标签是'np.arange(n)', 'n'代表data的元素个数\n",
    "- columns: 列标签，如果没有传递columns值，则默认列标签是'np.arange(n)'\n",
    "- dtype: 每一行的数据类型\n",
    "- copy: 默认为False, 表示复制数据data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93281fa3-401f-44b3-b707-d5e600623618",
   "metadata": {},
   "source": [
    "1) 创建空的DataFrame对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d0ff50d3-65ed-4e36-975e-52eb81b3c481",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "#创建空的DataFrame对象\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame()\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ab60b9-5c33-4077-ae6c-613ec8effed3",
   "metadata": {},
   "source": [
    "2) 列表创建DataFrame对象\n",
    "- 方式一: 单一列表创建DataFrame对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9e73673-693a-4380-9c4d-7a2200ab9ad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0\n",
      "0  1\n",
      "1  2\n",
      "2  3\n",
      "3  4\n",
      "4  5\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = [1,2,3,4,5]\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2625a010-3e4c-4b7a-8ffd-3907aea9570f",
   "metadata": {},
   "source": [
    "方式二：嵌套列表创建DataFrame对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8251b078-50d8-4836-82be-cce2a3a8cd7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Name  Age\n",
      "0         Qiyu   10\n",
      "1       Lishen   12\n",
      "2  Shenxinghui   13\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = [['Qiyu', 10], ['Lishen', 12], ['Shenxinghui', 13]]\n",
    "df = pd.DataFrame(data, columns=['Name', 'Age'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce3cd0b-bd44-4c17-9585-6e8c883018f7",
   "metadata": {},
   "source": [
    "方式三：指定数值元素的数据，例如类型为float"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83e6ea83-863b-4d7a-883d-e15514a2681c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          Name  Age\n",
      "0         Qiyu   10\n",
      "1       Lishen   12\n",
      "2  Shenxinghui   13\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = [['Qiyu', 10], ['Lishen', 12], ['Shenxinghui', 13]]\n",
    "df = pd.DataFrame(data, columns=['Name','Age'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8ffed71-7152-4c15-8c57-bb1f59c0c5f7",
   "metadata": {},
   "source": [
    "- 字典嵌套列表创建\n",
    "  - 字典中键对应的值元素必须相同（也就是列表的长度相同）如果传递了索引，那么索引的长度应该等于数组的长度；如果内有索引传递，那么默认情况，索引将是range(n),其中n代表数组长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e16e8f3-1fcc-4362-8d5a-ad44023968c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Name  Age\n",
      "0     Tom   23\n",
      "1   Jerry   34\n",
      "2   Steve   29\n",
      "3  Rabbit   42\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data ={'Name':['Tom','Jerry','Steve','Rabbit'], 'Age':[23,34,29,42]}\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "facf3170-a5d9-4190-8080-ed4447472ebf",
   "metadata": {},
   "source": [
    "【注意：这里使用了默认的标签，也就是range(n) 它生成了0，1，2，3，并分别对应了列表中的每个元素值】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "309296bb-375e-406f-bd8f-2ee0543d9b94",
   "metadata": {},
   "source": [
    "给上述案例加上自定义标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5e8ba843-17c8-4a09-bff1-6f368653a807",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Name  Age\n",
      "动漫人物1     Tom   23\n",
      "动漫人物2   Jerry   34\n",
      "动漫人物3   Steve   29\n",
      "动漫人物4  Rabbit   42\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data ={'Name':['Tom','Jerry','Steve','Rabbit'], 'Age':[23,34,29,42]}\n",
    "df = pd.DataFrame(data, index=['动漫人物1', '动漫人物2', '动漫人物3', '动漫人物4'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3eba5ebb-d77f-43cc-a663-0a6e139ab56e",
   "metadata": {},
   "source": [
    "【注意：Index参数为每行分配一个索引】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5dc854c8-0e3c-4364-9719-a12292802700",
   "metadata": {},
   "source": [
    "4) 列表嵌套字典创建DataFrame对象\n",
    "- 列表嵌套字典可以作为数据传递给DataFrame构造函数，默认情况，字典的键被用作列名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "944013d4-f019-4e26-a828-4ba7a8be2ed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   half-life2  combie  Alex\n",
      "0           1       2   NaN\n",
      "1           5      10  21.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = [{'half-life2': 1, 'combie': 2},{'half-life2': 5, 'combie': 10, 'Alex': 21}]\n",
    "df = pd.DataFrame(data)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5785e4ea-7cc3-4265-8a58-127b5fd1c055",
   "metadata": {},
   "source": [
    "给上面的案例添加行索引标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "32b5afbd-bef2-41cb-ad9d-9efd76a4efab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     half-life2  combie  Alex\n",
      "第一部           1       2   NaN\n",
      "第二部           5      10  21.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = [{'half-life2': 1, 'combie': 2},{'half-life2': 5, 'combie': 10, 'Alex': 21}]\n",
    "df = pd.DataFrame(data, index=['第一部', '第二部'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2a142b6-6808-4567-b01e-efd0e5818dd3",
   "metadata": {},
   "source": [
    "使用字典嵌套列表以及行、列索引创建一个DataFrame对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9347bcef-8571-4920-a66e-696be9e0a537",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     祁煜  戈登\n",
      "第一部   1   2\n",
      "第二部   5  10\n",
      "     祁煜  金沙群岛画家\n",
      "第一部   1     NaN\n",
      "第二部   5     NaN\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = [{'祁煜': 1, '戈登': 2},{'祁煜': 5, '戈登': 10, '黎深': 20}]\n",
    "df1 = pd.DataFrame(data, index=['第一部', '第二部'], columns=['祁煜', '戈登'])\n",
    "\n",
    "df2 = pd.DataFrame(data, index=['第一部', '第二部'], columns=['祁煜', '金沙群岛画家'])\n",
    "print(df1)\n",
    "print(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0574cabf-f740-4212-9184-9e3e0307c45b",
   "metadata": {},
   "source": [
    "【因为 金沙群岛画家 在字典中不存在，所以对应的值为NaN】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf06d6a-1a45-4ce5-a618-ad9c8d397e12",
   "metadata": {},
   "source": [
    "5) Series创建DataFrame对象\n",
    "- 也可以通过传递一个字典形式的Series,从而创建一个DataFrame对象，其输出结果的行索引是所有index的合集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "22f2e2f3-9f7f-424b-903c-50791eba65f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           one  two\n",
      "中美AI竞争     1.0  1.0\n",
      "八里沟        3.0  NaN\n",
      "贸易战        2.0  2.0\n",
      "马斯克脑机接口实现  NaN  3.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d = {'one': pd.Series([1, 2, 3],index=['中美AI竞争','贸易战','八里沟']),\n",
    "    'two': pd.Series([1, 2, 3], index=['中美AI竞争','贸易战','马斯克脑机接口实现'])}\n",
    "\n",
    "df = pd.DataFrame(d)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c899a6a-18b0-434e-9e13-94235266d9b0",
   "metadata": {},
   "source": [
    "【对于one列而言，此处虽然显示了行索引，但是由于没有对应的值，所以它的值为NaN】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8bbe61c-ad60-4743-9cd1-c543786bfa10",
   "metadata": {},
   "source": [
    "### 列表索引操作DataFrame\n",
    "- DataFrame可以使用列索引（columns index）来完成数据选取、添加和删除操作\n",
    "- "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "215fab6d-e518-43da-9f19-83cd478d69dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "乒乓球    3.0\n",
      "射击     1.0\n",
      "游泳     2.0\n",
      "网球     NaN\n",
      "跳水     NaN\n",
      "Name: 金牌, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d = {'金牌': pd.Series([1,2,3], index=['射击','游泳','乒乓球']),\n",
    "    '银牌': pd.Series([1,2,3], index=['射击','跳水','网球'])}\n",
    "df = pd.DataFrame(d)\n",
    "print(df['金牌'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd86f45-8363-4015-a6c1-6e21cf97cfc4",
   "metadata": {},
   "source": [
    "2) 列索引添加数据列\n",
    "- 使用columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c328f462-fa04-4d9a-b7a4-bb637ca87672",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         函数  概率论  数学必备知识\n",
      "函数基础一   1.0  NaN     NaN\n",
      "函数基础三   3.0  1.0     1.0\n",
      "函数基础二   2.0  NaN     NaN\n",
      "概率论基础一  NaN  2.0     2.0\n",
      "概率论基础二  NaN  3.0     3.0\n",
      "         函数  概率论  数学必备知识   考核\n",
      "函数基础一   1.0  NaN     NaN  NaN\n",
      "函数基础三   3.0  1.0     1.0  4.0\n",
      "函数基础二   2.0  NaN     NaN  NaN\n",
      "概率论基础一  NaN  2.0     2.0  NaN\n",
      "概率论基础二  NaN  3.0     3.0  NaN\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d = {'函数': pd.Series([1,2,3], index=['函数基础一', '函数基础二', '函数基础三']),\n",
    "    '概率论': pd.Series([1,2,3], index=['函数基础三', '概率论基础一', '概率论基础二'])}\n",
    "df = pd.DataFrame(d)\n",
    "\n",
    "#使用df['列']=值，插入新的数据列\n",
    "df['数学必备知识'] = pd.Series([1,2,3], index=['函数基础三', '概率论基础一', '概率论基础二'])\n",
    "print(df)\n",
    "#将已经存在的数据列做相加运算\n",
    "df['考核'] = df['函数'] + df['数学必备知识']\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31079eb3-8906-477d-a79d-87476d373530",
   "metadata": {},
   "source": [
    "使用DataFrame的算术运算，这和Numpy非常相似"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "883f9efd-550c-4216-a4c4-85f62e2da3af",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6c1dff4d-5e9d-474b-8523-29956692d3eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     姓名  年龄\n",
      "0  Qiyu  18\n",
      "1  Alen  19\n",
      "2  John  17\n",
      "     姓名  score  年龄\n",
      "0  Qiyu     98  18\n",
      "1  Alen     88  19\n",
      "2  John     34  17\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "info = [['Qiyu', 18], ['Alen', 19], ['John', 17]]\n",
    "df = pd.DataFrame(info, columns=['姓名', '年龄'])\n",
    "print(df)\n",
    "\n",
    "#注意columns参数\n",
    "#数值1代表插入到columns列表的索引位置\n",
    "df.insert(1, column='score', value=[98, 88, 34])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cce0633-ebb5-4b57-8885-b7e03f33cfd9",
   "metadata": {},
   "source": [
    "3) 列索引删除数据列\n",
    "- 通过del和pop()能够删除DataFrame中的数据列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2875b031-f301-4577-8a96-92b8363c016c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      金牌   银牌   铜牌\n",
      "乒乓球  3.0  NaN  NaN\n",
      "射击   1.0  1.0  NaN\n",
      "游泳   2.0  NaN  NaN\n",
      "田径   NaN  NaN  1.0\n",
      "网球   NaN  3.0  NaN\n",
      "跳水   NaN  2.0  NaN\n",
      "铁三   NaN  NaN  2.0\n",
      "马拉松  NaN  NaN  3.0\n",
      "      金牌   银牌\n",
      "乒乓球  3.0  NaN\n",
      "射击   1.0  1.0\n",
      "游泳   2.0  NaN\n",
      "田径   NaN  NaN\n",
      "网球   NaN  3.0\n",
      "跳水   NaN  2.0\n",
      "铁三   NaN  NaN\n",
      "马拉松  NaN  NaN\n",
      "      金牌\n",
      "乒乓球  3.0\n",
      "射击   1.0\n",
      "游泳   2.0\n",
      "田径   NaN\n",
      "网球   NaN\n",
      "跳水   NaN\n",
      "铁三   NaN\n",
      "马拉松  NaN\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d = {'金牌': pd.Series([1,2,3], index=['射击','游泳','乒乓球']),\n",
    "    '银牌': pd.Series([1,2,3], index=['射击','跳水','网球']),\n",
    "    '铜牌': pd.Series([1,2,3], index=['田径','铁三','马拉松'])}\n",
    "df = pd.DataFrame(d)\n",
    "print(df)\n",
    "#使用del删除\n",
    "del df['铜牌']\n",
    "print(df)\n",
    "\n",
    "#使用pop方法删除\n",
    "df.pop('银牌')\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b5ded60-c88d-4431-96d1-a1c521936e4d",
   "metadata": {},
   "source": [
    "### 行索引操作DataFrame\n",
    "1) 标签索引选取\n",
    "- 可以将行标签传递给loc函数，来选取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "08ae9774-0dfe-4031-9d57-b98062290ab8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "金牌    2.0\n",
      "银牌    NaN\n",
      "铜牌    NaN\n",
      "Name: 游泳, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d = {'金牌': pd.Series([1,2,3], index=['射击','游泳','乒乓球']),\n",
    "    '银牌': pd.Series([1,2,3], index=['射击','跳水','网球']),\n",
    "    '铜牌': pd.Series([1,2,3], index=['田径','铁三','马拉松'])}\n",
    "df = pd.DataFrame(d)\n",
    "print(df.loc['游泳'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1888fd5-c7ca-4696-a8b6-ec2b4a2f44e5",
   "metadata": {},
   "source": [
    "【loc允许接两个参数，分别是行和列，参数之间需要使用逗号隔开，但该函数只能接收标签索引】"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5acfff4a-d893-4582-abb9-c50be2635e78",
   "metadata": {},
   "source": [
    "2) 整数索引选取\n",
    "- 通过将数据所在的索引位置传递给iloc()函数，同样可以实现数据行选取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6745a074-85b2-4ba7-8cd6-c3811d1cda4a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "金牌    2.0\n",
      "银牌    NaN\n",
      "铜牌    NaN\n",
      "Name: 游泳, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d = {'金牌': pd.Series([1,2,3], index=['射击','游泳','乒乓球']),\n",
    "    '银牌': pd.Series([1,2,3], index=['射击','跳水','网球']),\n",
    "    '铜牌': pd.Series([1,2,3], index=['田径','铁三','马拉松'])}\n",
    "df = pd.DataFrame(d)\n",
    "print(df.iloc[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7495423b-7d53-4ac5-bff5-777d6cf38937",
   "metadata": {},
   "source": [
    "3) 切片操作多行选取\n",
    "- 可以使用切片的方式同时选取多行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "261a14b4-92e1-442d-8900-92bfecbc4f90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     金牌  银牌   铜牌\n",
      "游泳  2.0 NaN  NaN\n",
      "田径  NaN NaN  1.0\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "d = {'金牌': pd.Series([1,2,3], index=['射击','游泳','乒乓球']),\n",
    "    '银牌': pd.Series([1,2,3], index=['射击','跳水','网球']),\n",
    "    '铜牌': pd.Series([1,2,3], index=['田径','铁三','马拉松'])}\n",
    "df = pd.DataFrame(d)\n",
    "#左闭右开\n",
    "print(df[2:4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "948c7f71-f2be-4014-b6ae-868b979b1918",
   "metadata": {},
   "source": [
    "4) 添加数据行\n",
    "- 对于数据追加来说，有多种方法，其中包括.concat()、.loc()、.append()。其中，.appene()已经不推荐使用，但是依然能够完成对数据的操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e851337c-e18b-4618-a9e9-413e6471409b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b\n",
      "0  1  2\n",
      "1  3  4\n",
      "2  5  6\n"
     ]
    }
   ],
   "source": [
    "#使用concat()方法完成数据的添加\n",
    "import pandas as pd\n",
    "df = pd.DataFrame([[1,2],[3,4]], columns=['a','b'])\n",
    "\n",
    "#要添加的数据行，创建一个新的DataFrame\n",
    "new_df = pd.DataFrame([[5,6]], columns=['a','b'])\n",
    "\n",
    "#使用pd.concat()合并\n",
    "df = pd.concat([df, new_df], ignore_index=True)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8dca896e-954d-406d-a27a-c6d7e3b0ec1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B\n",
      "0  1  2\n",
      "1  3  4\n",
      "2  5  6\n"
     ]
    }
   ],
   "source": [
    "#如果要添加单个的行，可以使用loc()\n",
    "import pandas as pd\n",
    "\n",
    "#创建一个DataFrame\n",
    "df = pd.DataFrame([[1,2], [3,4]], columns=['A', 'B'])\n",
    "\n",
    "#直接使用loc()方法添加行\n",
    "df.loc[len(df)] = [5,6]\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8a7624f-631d-4538-bb1c-2dbb6bb5af3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用append()添加新行，但是在最新版的使用中会报错,所以不推荐使用\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame([[1,2], [3,4]], columns=['A', 'B'])\n",
    "\n",
    "#要添加的新行\n",
    "new_row = {'A':5, 'B':6}\n",
    "\n",
    "#使用append()添加新行，但是在最新版的使用中会报错\n",
    "df = df.append(new_row, ignore_index=True)\n",
    "\n",
    "print(df)\n",
    "#报错：\n",
    "#AttributeError: 'DataFrame' object has no attribute 'append'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d8613f5-373c-4533-8e59-28a9f662081e",
   "metadata": {},
   "source": [
    "5) 添加数据列"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "740a47ba-7d95-4951-af95-25b89a3fe94a",
   "metadata": {},
   "source": [
    "(1).直接赋值，如果列不存在Pandas会自动创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1ca8a8a3-186a-430b-96ee-8e27c39ffa88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B  C\n",
      "0  1  4  7\n",
      "1  2  5  8\n",
      "2  3  6  9\n"
     ]
    }
   ],
   "source": [
    "#直接赋值，如果列不存在Pandaas会自动创建\n",
    "import pandas as pd\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3],\n",
    "    'B': [4,5,6]\n",
    "})\n",
    "\n",
    "#添加新列C\n",
    "df['C'] = [7,8,9]\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8615c33-e3e5-41b8-829e-0eb4a347498f",
   "metadata": {},
   "source": [
    "(2).使用assign()方法\n",
    "- assign()可以创建新的列，并返回一个新的DataFrame,这种方法的好处是可以在一次调用中添加多个列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "11d830bb-36af-47cf-aa9e-e2fb6b449247",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B  C  D\n",
      "0  1  4  7  5\n",
      "1  2  5  8  7\n",
      "2  3  6  9  9\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3],\n",
    "    'B': [4,5,6]\n",
    "})\n",
    "\n",
    "#使用assign()添加新列C 和 D\n",
    "df = df.assign(C=[7,8,9], D=lambda x: x['A'] + x['B'])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25686516-a2e8-45f5-993f-ea738bf09c3f",
   "metadata": {},
   "source": [
    "(3).使用insert()方法\n",
    "- 允许在指定的位置插入新列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ab4b1a9-b67e-4568-a336-f0f323273f94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  C  B\n",
      "0  1  7  4\n",
      "1  2  8  5\n",
      "2  3  9  6\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3],\n",
    "    'B': [4,5,6]\n",
    "})\n",
    "\n",
    "#在位置1 插入新列\n",
    "df.insert(1, 'C', [7,8,9])\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75daf564-cc8c-449d-85d0-278510242f1d",
   "metadata": {},
   "source": [
    "(4). apply() 方法\n",
    "- 根据现有列计算新列的值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "53c1e1fe-23f7-495d-bfdb-83cc22d98aa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B   C\n",
      "0  1  4   4\n",
      "1  2  5  10\n",
      "2  3  6  18\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3],\n",
    "    'B': [4,5,6]\n",
    "})\n",
    "\n",
    "#使用apply()添加新列C，其值为A 和 B 的乘积\n",
    "df['C'] = df.apply(lambda row: row['A'] * row['B'], axis=1)\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34de6da9-993f-4285-b628-8771b22b5ac2",
   "metadata": {},
   "source": [
    "(5).eval()方法\n",
    "- 执行字符串表达式，从而创建新的列。可以直接识别字符串中的 + - * / 等各种计算符号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "dd6b1e21-50c0-4a9b-b645-9af28e6db265",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B     C  D\n",
      "0  1  4  0.25  5\n",
      "1  2  5  0.40  7\n",
      "2  3  6  0.50  9\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3],\n",
    "    'B': [4,5,6]\n",
    "})\n",
    "\n",
    "#使用eval()添加新的列 C，其值为 A 和 B 的商\n",
    "df['C'] = df.eval('A / B')    # A // B 保留整数， A / B 保留小数\n",
    "#使用eval()添加新的列 C，其值为 A 和 B 的和\n",
    "df['D'] = df.eval('A + B')\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c13d32b3-c845-4504-9bef-b2dc5c6e2a24",
   "metadata": {},
   "source": [
    "6) 删除行\n",
    "- (1)可以使用行索引标签，从DataFrame中删除行数据\n",
    "- drop(), 指定传入要删除的索引进行删除"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6eabe462-a21f-4b95-b7d9-e18336bcfd94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b\n",
      "0  1  2\n",
      "1  3  4\n",
      "2  5  6\n",
      "3  7  8\n",
      "   a  b\n",
      "1  3  4\n",
      "2  5  6\n",
      "3  7  8\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame([[1,2],[3,4]], columns=['a','b'])\n",
    "df2 = pd.DataFrame([[5,6],[7,8]], columns=['a','b'])\n",
    "\n",
    "df = pd.concat([df, df2], ignore_index=True)\n",
    "print(df)\n",
    "#调用drop()方法\n",
    "df = df.drop(0)\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852e4ee7-f8d8-4890-a46a-e0a8a04bb7ce",
   "metadata": {},
   "source": [
    "(2) drop()删除多行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8529495c-c7a6-40e3-aaee-6a7497e17829",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A  B   C\n",
      "第一行  1  5   9\n",
      "第四行  4  8  12\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3,4],\n",
    "    'B': [5,6,7,8],\n",
    "    'C': [9,10,11,12]},index=['第一行','第二行','第三行','第四行'])\n",
    "\n",
    "#删除索引为 '第二行' 和 ' 第三行' 的【行】\n",
    "df = df.drop(['第二行','第三行'])\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "04c30937-34d6-4508-8040-987b7b326ddf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A  B   C\n",
      "第一行  1  5   9\n",
      "第二行  2  6  10\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3,4],\n",
    "    'B': [5,6,7,8],\n",
    "    'C': [9,10,11,12]},index=['第一行','第二行','第三行','第四行'])\n",
    "#删除 A 列值大于 2 的行\n",
    "df = df[df['A'] <= 2]\n",
    "print(df)\n",
    "#可以看出，大于2的行都被删除了"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decb8a7b-5137-4ac5-b8c2-bc98cf7993ab",
   "metadata": {},
   "source": [
    "(3). query()根据条件删除行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36ab73fd-10bd-4011-950f-69529401a12f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   A  B   C\n",
      "0  1  5   9\n",
      "1  2  6  10\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3,4],\n",
    "    'B': [5,6,7,8],\n",
    "    'C': [9,10,11,12]})\n",
    "\n",
    "#删除 A 列值大于 2 的行\n",
    "df = df.query('A <= 2')\n",
    "\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1c6672a-a5a5-4b72-ad13-579a3603a2ff",
   "metadata": {},
   "source": [
    "(4). index() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0342b451-06e0-421d-99c0-fbfea27d99fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     A  B   C\n",
      "第一行  1  5   9\n",
      "第三行  3  7  11\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame({\n",
    "    'A': [1,2,3,4],\n",
    "    'B': [5,6,7,8],\n",
    "    'C': [9,10,11,12]},index=['第一行','第二行','第三行','第四行'])\n",
    "\n",
    "#删除索引为 '第二行' 和 '第四行' 的行\n",
    "df = df[~df.index.isin(['第二行', '第四行'])]\n",
    "\n",
    "print(df)"
   ]
  }
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